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Found 25 papers, 7 papers with code
Gaze-Driven Sentence Simplification for Language Learners: Enhancing Comprehension and Readability
Language learners should regularly engage in reading challenging materials as part of their study routine.
Pointing out Human Answer Mistakes in a Goal-Oriented Visual Dialogue
Effective communication between humans and intelligent agents has promising applications for solving complex problems.
Enhancing Perception and Immersion in Pre-Captured Environments through Learning-Based Eye Height Adaptation
To explore this influence, we first propose a pilot study that captures real environments with multiple eye heights and asks participants to judge the egocentric distances and immersion.
Audio-Visual Speech Enhancement With Selective Off-Screen Speech Extraction
This paper describes an audio-visual speech enhancement (AV-SE) method that estimates from noisy input audio a mixture of the speech of the speaker appearing in an input video (on-screen target speech) and of a selected speaker not appearing in the video (off-screen target speech).
Improving the Gap in Visual Speech Recognition Between Normal and Silent Speech Based on Metric Learning
This paper presents a novel metric learning approach to address the performance gap between normal and silent speech in visual speech recognition (VSR).
Memory Efficient Diffusion Probabilistic Models via Patch-based Generation
Second, we propose Global Content Conditioning (GCC) to ensure patches have coherent content when concatenated together.
Scapegoat Generation for Privacy Protection from Deepfake
To protect privacy and prevent malicious use of deepfake, current studies propose methods that interfere with the generation process, such as detection and destruction approaches.
Event-based Camera Simulation using Monte Carlo Path Tracing with Adaptive Denoising
This paper presents an algorithm to obtain an event-based video from noisy frames given by physics-based Monte Carlo path tracing over a synthetic 3D scene.
Geometric Features Informed Multi-person Human-object Interaction Recognition in Videos
Consider that geometric features such as human pose and object position provide meaningful information to understand HOIs, we argue to combine the benefits of both visual and geometric features in HOI recognition, and propose a novel Two-level Geometric feature-informed Graph Convolutional Network (2G-GCN).
The Sound of Bounding-Boxes
To tackle this problem, we propose a fully unsupervised method that learns to detect objects in an image and separate sound source simultaneously.
Community-Driven Comprehensive Scientific Paper Summarization: Insight from cvpaper.challenge
To alleviate this problem, we organized a group of non-native English speakers to write summaries of papers presented at a computer vision conference to share the knowledge of the papers read by the group.
360 Depth Estimation in the Wild -- The Depth360 Dataset and the SegFuse Network
In this work, we first establish a large-scale dataset with varied settings called Depth360 to tackle the training data problem.
RLTutor: Reinforcement Learning Based Adaptive Tutoring System by Modeling Virtual Student with Fewer Interactions
However, optimization using reinforcement learning requires a large number of interactions, and thus it cannot be applied directly to actual students.
LSTM-SAKT: LSTM-Encoded SAKT-like Transformer for Knowledge Tracing
This paper introduces the 2nd place solution for the Riiid!
Self-Supervised Learning for Visual Summary Identification in Scientific Publications
Providing visual summaries of scientific publications can increase information access for readers and thereby help deal with the exponential growth in the number of scientific publications.
Do We Need Sound for Sound Source Localization?
Moreover, we show that the majority of sound-producing objects within the samples in this dataset can be inherently identified using only visual information, and thus that the dataset is inadequate to evaluate a system's capability to leverage aural information.
MirrorNet: A Deep Bayesian Approach to Reflective 2D Pose Estimation from Human Images
Specifically, we formulate a hierarchical generative model of poses and images by integrating a deep generative model of poses from pose features with that of images from poses and image features.
Learning with Protection: Rejection of Suspicious Samples under Adversarial Environment
Our main idea is to apply a framework of learning with rejection and adversarial examples to assist in the decision making for such suspicious samples.
What Do Adversarially Robust Models Look At?
In this paper, we address the open question: "What do adversarially robust models look at?"
PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization
We introduce Pixel-aligned Implicit Function (PIFu), a highly effective implicit representation that locally aligns pixels of 2D images with the global context of their corresponding 3D object.
Ranked #1 on
3D Object Reconstruction
on RenderPeople
SiCloPe: Silhouette-Based Clothed People
The synthesized silhouettes which are the most consistent with the input segmentation are fed into a deep visual hull algorithm for robust 3D shape prediction.
FSNet: An Identity-Aware Generative Model for Image-based Face Swapping
We herein represent the face region with a latent variable that is assigned with the proposed deep neural network (DNN) instead of facial textures.
Automatic Paper Summary Generation from Visual and Textual Information
Due to the recent boom in artificial intelligence (AI) research, including computer vision (CV), it has become impossible for researchers in these fields to keep up with the exponentially increasing number of manuscripts.
Understanding Fake Faces
Face recognition research is one of the most active topics in computer vision (CV), and deep neural networks (DNN) are now filling the gap between human-level and computer-driven performance levels in face verification algorithms.
RSGAN: Face Swapping and Editing using Face and Hair Representation in Latent Spaces
The proposed network independently handles face and hair appearances in the latent spaces, and then, face swapping is achieved by replacing the latent-space representations of the faces, and reconstruct the entire face image with them.
